Image Processing and Computer Vision (CS-803 (A)) - Important Questions

Explain the concept of computer vision and its role/necessity in image processing.

Define digital image and explain image digitization via sampling and quantization.

Discuss the evolution and history of Computer Vision and Image Processing (CVIP). How have advancements in technology influenced the development of CVIP?

Explain the steps involved in image conditioning, labeling, and grouping for image recognition.

Define morphological image processing and describe the operations of dilation and erosion on binary images.

Describe thinning and thickening morphological operations and give a useful application example.

Explain the different types of computer vision models and their applications in detail.

Discuss the history and evolution of Computer Vision and Image Processing (CVIP).

Explain the significance of image representations and image filtering.

Discuss various color vision models.

Define morphological image processing and describe its primary operations.

What are the components of a digital image processing system? Explain each in detail.

Explain image segmentation and discuss thresholding and connected component labeling techniques.

Explain boundary descriptors in image processing in detail.

Explain the Hough transform with suitable derivations.

Explain boundary and region descriptors in image processing and their importance in image analysis.

Define image restoration, compression, segmentation, and morphological processing.

Describe the split-and-merge technique used in hierarchical segmentation.

Explain edge detection and the Hough transform for area extraction.

Describe the least-square fitting technique in curve fitting and its applications.

Explain how the line is detected in the image and give the masks that are used to detect it?

Explain segmentation techniques based on finding regions directly.

What are the various representation schemes of digital image processing? Explain.

What is thresholding, its uses and limitations?

Explain spatial moments and gray-level moments in region analysis and how they are used in object recognition.

Explain the distance and the distance relational approach in the image matching framework, and compare it with ordered structural matching.

Explain boundary analysis and the properties used in analyzing boundaries, including signatures and shape numbers.

Discuss how to measure the properties of the region in a labeled image?

Explain the backtracking algorithm and illustrate its application with an example such as consistency labelling or shape recognition.

Explain perspective projective geometry and how it / inverse perspective projection enables interpretation of 3D objects from 2D images.

Describe the facet model recognition method. How is it applied to shape classification in images?

Define inverse perspective projection and shape numbers used in boundary analysis.

Explain the inverse perspective projection algorithm.

Explain in brief how photogrammetric processing is done from 2D to 3D?

What is image intensity in image processing? Discuss in brief the Intensity matching of ID signals in an image.

Explain the importance of shape recognition in image processing.

Compare weak perspective projection and orthographic projection in affine camera models.

Explain how neural network structures are used for pattern / image shape recognition with an example.

Explain the Hough transform and its use for detecting simple objects / simple object recognition.

What is Principal Component Analysis (PCA) and its role in feature extraction for object recognition?

Write short notes on machine learning and neural networks in image shape recognition, Knowledge-Based Vision, and Curve Fitting.

Write brief notes on feature extraction, knowledge representation, and backtracking algorithm.

Explain what knowledge-based vision is and how it supports object recognition.

Give a brief summary of object recognition methods.

Define and explain knowledge representation and Information Integration.

How to evaluate extracted shape descriptors in 3D vision? Discuss.

Explain identifying road signs in a vehicle vision system.

Discuss accurate centre location using the Hough transform.

Discuss the procedure for conversion from RGB color model to HIS color model.